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Polynomial Regression Calculator

Fit linear through sextic polynomial regression models to data points with R-squared and coefficient display.

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What Is Polynomial Regression?

Polynomial regression fits a curved relationship between an independent variable $x$ and a dependent variable $y$ using a polynomial of chosen degree. Degree 1 is linear regression; higher degrees capture bends and peaks. See also the Linear Regression Calculator and Exponential Regression Calculator.

Polynomial Regression Equation

$$y = a_0 + a_1 x + a_2 x^2 + \cdots + a_n x^n$$

Coefficients are found by least squares: minimize the sum of squared residuals between observed and predicted $y$ values. You need at least $n + 1$ points to fit a degree-$n$ model.

Interpreting R²

The coefficient of determination $R^2$ measures how much variation in $y$ is explained by the polynomial model. Values closer to 1 indicate a better fit, but very high-degree models can overfit small datasets.

Frequently Asked Questions

How many points do I need?

You need at least one more point than the polynomial degree. For a cubic model (degree 3), enter at least 4 (x, y) pairs.

Is polynomial regression linear?

Yes in the statistical sense: the model is linear in the coefficients even though the curve itself can be nonlinear in $x$.

What if the fit fails?

A singular matrix usually means duplicate x values, too few points, or a degree that is too high for your data.

Which degree should I choose?

Start with degree 2 or 3 for smooth curves. Increase the degree only when a simpler model clearly underfits.